
We introduce a diffusion-based approach for dance generation that combines state-of-the-art performance with powerful editing capabilities and is able to generate arbitrarily long sequences.

We analyze the metrics proposed in previous works and show that they do not accurately represent human-evaluated quality as reported by a large user study.

We propose a new approach to eliminating foot-sliding physical implausibilities in generated motions using a novel Contact Consistency Loss, and introduce Physical Foot Contact Score, a simple new acceleration-based quantitative metric for scoring physical plausibility of generated kinematic motions that requires no explicit physical modeling.

We improve on previous hand-crafted audio feature extraction strategies by leveraging music audio representations from Jukebox [1]}, a pre-trained generative model for music that has previously demonstrated strong performance on music-specific prediction tasks [2]}, [3]}.

